There exist a number of emerging applications that make use of large scale multicast groups on the Internet. Such emerging applications include database replication, service discovery, distributed interactive simulations, and large scale voice and video conferencing. In many cases, these applications require each participant in the multicast group to keep a count of the other members in the multicast group. Such counts are often necessary for scaling feedback in order to perform congestion control or reliable multicast. Additionally, the number of participants in a multicast group is often dynamic, increasing and decreasing sharply at times. For example, in a multimedia conference, there will be a sharp increase in the number of participants when the session begins, and a sharp decrease in the number of participants when the session ends.
In order to effectively count the number of other participants, each participant may maintain a participant list that contains every other member of the multicast group heard from. For truly large scale applications, however, low power devices such as a telephone or TV set top boxes cannot adequately support sufficient memory to maintain a listing of each participant or group member.
The prior art has generally addressed this problem by employing various sampling mechanisms. These prior art sampling mechanisms store only a subset of the group members, yet allow a participant to keep a relatively accurate group size estimate. Specifically, the prior art has employed statistical sampling mechanisms in which group members maintain keys that are random and unique. Each participant in the group maintains a mask, with some number of bits, m, set. If the key of a participant matches the key of the local participant under the masking operation, the participant identifier is kept in the table of the local participant, otherwise its identifier is discarded. Additionally, the prior art has shown how the number of bits in the mask can be increased as group sizes grow, thereby minimizing the variance in the group size estimate for a given memory size. Despite such developments however, the prior art has not demonstrated an effective way to decrease the number of bits in the mask--an essential characteristic when the group membership decreases. Instead, the prior art has relied upon "corrective fudge factors" to compensate for the sampling errors. Unfortunately, however, these factors are inaccurate for those situations where group size decreases rapidly and oftentimes these factors may require a potentially unbounded number of mathematical operations if the group size oscillates rapidly.
Consequently, alternative methods of tracking group members of connectionless network activities are required.